{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lqRGAV4PrwqL",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Introduction to BlenderProc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y-i21RQVPngI",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Note: This notebook makes use of the basic example which is available under `examples/basic`\n",
    "\n",
    "In this notebook, we will see how can we quickly set up the BlenderProc environment inside Google Colab and how can we generate photorealistic data which can later be used for many different applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-mShvZYWrRS",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We firstly clone the official BlenderProc repo (repository) from GitHub using Git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f5AICxd_z5-V",
    "outputId": "eb673822-b87a-4b56-fe38-27404aaecb0e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'BlenderProc'...\n",
      "remote: Enumerating objects: 39125, done.\u001b[K\n",
      "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
      "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
      "remote: Total 39125 (delta 3), reused 5 (delta 1), pack-reused 39107\u001b[K\n",
      "Receiving objects: 100% (39125/39125), 86.37 MiB | 15.91 MiB/s, done.\n",
      "Resolving deltas: 100% (29442/29442), done.\n",
      "/content/BlenderProc\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/DLR-RM/BlenderProc.git\n",
    "%cd \"BlenderProc\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kMuWiS6k1ajB",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "To be able to use the blenderproc command, we install it via pip:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WAIBtiW0WC3y",
    "outputId": "c2ab8da3-cb24-4e97-e337-dc9d0c2b19d7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///content/BlenderProc\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (57.4.0)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (3.13)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (2.23.0)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (3.2.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (1.21.6)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (7.1.2)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from blenderproc==2.3.0) (3.1.0)\n",
      "Collecting progressbar\n",
      "  Downloading progressbar-2.5.tar.gz (10 kB)\n",
      "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->blenderproc==2.3.0) (1.5.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->blenderproc==2.3.0) (2.8.2)\n",
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      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->blenderproc==2.3.0) (3.0.8)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->blenderproc==2.3.0) (0.11.0)\n",
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      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->blenderproc==2.3.0) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->blenderproc==2.3.0) (2021.10.8)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->blenderproc==2.3.0) (1.24.3)\n",
      "Building wheels for collected packages: progressbar\n",
      "  Building wheel for progressbar (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for progressbar: filename=progressbar-2.5-py3-none-any.whl size=12082 sha256=aecda2d7819cee744a7613fc8bb5c0024e981a7a6b4f4f1d0bbbe07016fa188a\n",
      "  Stored in directory: /root/.cache/pip/wheels/f0/fd/1f/3e35ed57e94cd8ced38dd46771f1f0f94f65fec548659ed855\n",
      "Successfully built progressbar\n",
      "Installing collected packages: progressbar, blenderproc\n",
      "  Running setup.py develop for blenderproc\n",
      "Successfully installed blenderproc-2.3.0 progressbar-2.5\n"
     ]
    }
   ],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BXlzLifCR80c",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "In order to run BlenderProc inside Google Colab, we first have to update the `LD_PRELOAD` environment variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "BMFdNLxNBO0R",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# updating the LD_PRELOAD env variable\n",
    "os.environ[\"LD_PRELOAD\"] = \"/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4.3.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mj0wfiFyCIKR",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "To be able to use matplotlib inside BlenderProc, we have to install ipykernel inside blender's python environment. As this is the first blenderproc command, it will also install blender first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_VWPCNhV9Bjt",
    "outputId": "e4a1ebc4-7d50-491e-c844-d2db13afc4fa",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading blender from https://download.blender.org/release/Blender3.1/blender-3.1.0-linux-x64.tar.xz\n",
      "100% ||\n",
      "Using blender in ./blender-3.1.0-linux-x64\n",
      "Looking in links: /tmp/tmpty2amnkm\n",
      "Processing /tmp/tmpty2amnkm/setuptools-58.1.0-py3-none-any.whl\n",
      "Processing /tmp/tmpty2amnkm/pip-21.2.4-py3-none-any.whl\n",
      "Installing collected packages: setuptools, pip\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "blenderproc 2.3.0 requires h5py, which is not installed.\n",
      "blenderproc 2.3.0 requires matplotlib, which is not installed.\n",
      "blenderproc 2.3.0 requires numpy, which is not installed.\n",
      "blenderproc 2.3.0 requires Pillow, which is not installed.\n",
      "blenderproc 2.3.0 requires progressbar, which is not installed.\n",
      "blenderproc 2.3.0 requires pyyaml, which is not installed.\n",
      "blenderproc 2.3.0 requires requests, which is not installed.\u001b[0m\n",
      "Successfully installed pip-21.2.4 setuptools-58.1.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Requirement already satisfied: pip in ./blender-3.1.0-linux-x64/3.1/python/lib/python3.10/site-packages (21.2.4)\n",
      "Collecting pip\n",
      "  Downloading pip-22.0.4-py3-none-any.whl (2.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 2.1 MB 5.5 MB/s \n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 21.2.4\n",
      "    Uninstalling pip-21.2.4:\n",
      "      Successfully uninstalled pip-21.2.4\n",
      "Successfully installed pip-22.0.4\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Installing pip package ipykernel None\n",
      "Collecting ipykernel\n",
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      "Using legacy 'setup.py install' for tornado, since package 'wheel' is not installed.\n",
      "Installing collected packages: wcwidth, pure-eval, ptyprocess, pickleshare, executing, backcall, traitlets, tornado, six, pyzmq, pyparsing, pygments, psutil, prompt-toolkit, pexpect, parso, nest-asyncio, entrypoints, decorator, debugpy, python-dateutil, packaging, matplotlib-inline, jupyter-core, jedi, asttokens, stack-data, jupyter-client, ipython, ipykernel\n",
      "  Running setup.py install for tornado ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[33m  WARNING: The script pygmentize is installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The scripts jupyter, jupyter-migrate and jupyter-troubleshoot are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The scripts jupyter-kernel, jupyter-kernelspec and jupyter-run are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The scripts ipython and ipython3 are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0mSuccessfully installed asttokens-2.0.5 backcall-0.2.0 debugpy-1.6.0 decorator-5.1.1 entrypoints-0.4 executing-0.8.3 ipykernel-6.13.0 ipython-8.2.0 jedi-0.18.1 jupyter-client-7.2.2 jupyter-core-4.10.0 matplotlib-inline-0.1.3 nest-asyncio-1.5.5 packaging-21.3 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 prompt-toolkit-3.0.29 psutil-5.9.0 ptyprocess-0.7.0 pure-eval-0.2.2 pygments-2.12.0 pyparsing-3.0.8 python-dateutil-2.8.2 pyzmq-22.3.0 six-1.16.0 stack-data-0.2.0 tornado-6.1 traitlets-5.1.1 wcwidth-0.2.5\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!blenderproc pip install ipykernel --blender-install-path ./"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Lz4nvzETWXU",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Finally, we run the BlenderProc program using the `blenderproc run`  along with required command line arguments. The first argument specifies the location of the python file that should be executed. The second argument corresponds to the camera pose file. In this case, we have specified two camera poses in the `examples/basics/basic/camera_positions` file. The third argument correponds to the output directory where our generated data will be stored. \n",
    "\n",
    "With the flag `--blender-install-path`, we specify the custom Blender install path which is necessary, as no user folder is availabe in colab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jVZTximT08tN",
    "outputId": "2a1f7af9-8575-45f8-e102-2b212ab192ec",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using blender in ./blender-3.1.0-linux-x64\n",
      "Using temporary directory: /dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff\n",
      "Blender 3.1.0 (hash c77597cd0e15 built 2022-03-09 00:34:48)\n",
      "Installing pip package wheel None\n",
      "Installing pip package wheel None\n",
      "Collecting wheel\n",
      "  Downloading wheel-0.37.1-py2.py3-none-any.whl (35 kB)\n",
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      "\u001b[33m  WARNING: The script wheel is installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0mSuccessfully installed wheel-0.37.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package pyyaml 5.1.2\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Building wheels for collected packages: pyyaml\n",
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      "  Created wheel for pyyaml: filename=PyYAML-5.1.2-cp310-cp310-linux_x86_64.whl size=44116 sha256=40dcab5ffae2418574c809da1f2b672c8d7e90598695980d42616ac8eae67908\n",
      "  Stored in directory: /root/.cache/pip/wheels/1c/77/3e/dcf9cc5e235189dedcf5f1736a14caaac20267a5bb846c8ce1\n",
      "Successfully built pyyaml\n",
      "Installing collected packages: pyyaml\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "blenderproc 2.3.0 requires h5py, which is not installed.\n",
      "blenderproc 2.3.0 requires matplotlib, which is not installed.\n",
      "blenderproc 2.3.0 requires numpy, which is not installed.\n",
      "blenderproc 2.3.0 requires Pillow, which is not installed.\n",
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      "\u001b[0mSuccessfully installed pyyaml-5.1.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package imageio 2.9.0\n",
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      "\u001b[?25hInstalling collected packages: pillow, numpy, imageio\n",
      "\u001b[33m  WARNING: The scripts f2py, f2py3 and f2py3.10 are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The scripts imageio_download_bin and imageio_remove_bin are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "blenderproc 2.3.0 requires h5py, which is not installed.\n",
      "blenderproc 2.3.0 requires matplotlib, which is not installed.\n",
      "blenderproc 2.3.0 requires progressbar, which is not installed.\n",
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      "\u001b[0mSuccessfully installed imageio-2.9.0 numpy-1.22.3 pillow-9.1.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package gitpython 3.1.18\n",
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      "\u001b[?25hCollecting smmap<6,>=3.0.1\n",
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      "Installing collected packages: smmap, gitdb, gitpython\n",
      "Successfully installed gitdb-4.0.9 gitpython-3.1.18 smmap-5.0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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      "Collecting PyWavelets>=1.1.1\n",
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      "Collecting scipy>=1.4.1\n",
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      "Installing collected packages: tifffile, scipy, PyWavelets, networkx, scikit-image\n",
      "\u001b[33m  WARNING: The scripts lsm2bin, tiff2fsspec, tiffcomment and tifffile are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The script skivi is installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
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      "\u001b[0mSuccessfully installed PyWavelets-1.3.0 networkx-2.8 scikit-image-0.19.2 scipy-1.8.0 tifffile-2022.4.22\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package pypng 0.0.20\n",
      "Collecting pypng==0.0.20\n",
      "  Downloading pypng-0.0.20-py3-none-any.whl (70 kB)\n",
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      "\u001b[?25hInstalling collected packages: pypng\n",
      "Successfully installed pypng-0.0.20\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package scipy 1.7.3\n",
      "Collecting scipy==1.7.3\n",
      "  Downloading scipy-1.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.9 MB)\n",
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      "Installing collected packages: scipy\n",
      "  Attempting uninstall: scipy\n",
      "    Found existing installation: scipy 1.8.0\n",
      "    Uninstalling scipy-1.8.0:\n",
      "      Successfully uninstalled scipy-1.8.0\n",
      "Successfully installed scipy-1.7.3\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package matplotlib 3.5.1\n",
      "Collecting matplotlib==3.5.1\n",
      "  Downloading matplotlib-3.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.9 MB)\n",
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      "\u001b[?25hCollecting kiwisolver>=1.0.1\n",
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      "Requirement already satisfied: packaging>=20.0 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from matplotlib==3.5.1) (21.3)\n",
      "Requirement already satisfied: six>=1.5 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.1) (1.16.0)\n",
      "Installing collected packages: kiwisolver, fonttools, cycler, matplotlib\n",
      "\u001b[33m  WARNING: The scripts fonttools, pyftmerge, pyftsubset and ttx are installed in '/content/BlenderProc/blender-3.1.0-linux-x64/custom-python-packages/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "blenderproc 2.3.0 requires h5py, which is not installed.\n",
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      "blenderproc 2.3.0 requires requests, which is not installed.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed cycler-0.11.0 fonttools-4.33.2 kiwisolver-1.4.2 matplotlib-3.5.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package pytz 2021.1\n",
      "Collecting pytz==2021.1\n",
      "  Downloading pytz-2021.1-py2.py3-none-any.whl (510 kB)\n",
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      "\u001b[?25hInstalling collected packages: pytz\n",
      "Successfully installed pytz-2021.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package h5py 3.6.0\n",
      "Collecting h5py==3.6.0\n",
      "  Downloading h5py-3.6.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from h5py==3.6.0) (1.22.3)\n",
      "Installing collected packages: h5py\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "blenderproc 2.3.0 requires progressbar, which is not installed.\n",
      "blenderproc 2.3.0 requires requests, which is not installed.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed h5py-3.6.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package pillow 8.3.2\n",
      "Collecting Pillow==8.3.2\n",
      "  Downloading Pillow-8.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
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      "\u001b[?25hInstalling collected packages: Pillow\n",
      "  Attempting uninstall: Pillow\n",
      "    Found existing installation: Pillow 9.1.0\n",
      "    Uninstalling Pillow-9.1.0:\n",
      "      Successfully uninstalled Pillow-9.1.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "blenderproc 2.3.0 requires progressbar, which is not installed.\n",
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      "\u001b[0mSuccessfully installed Pillow-8.3.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package opencv-contrib-python 4.5.5.64\n",
      "Collecting opencv-contrib-python==4.5.5.64\n",
      "  Downloading opencv_contrib_python-4.5.5.64-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (66.7 MB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.21.2 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from opencv-contrib-python==4.5.5.64) (1.22.3)\n",
      "Installing collected packages: opencv-contrib-python\n",
      "Successfully installed opencv-contrib-python-4.5.5.64\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package scikit-learn 1.0.2\n",
      "Collecting scikit-learn==1.0.2\n",
      "  Downloading scikit_learn-1.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.5 MB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.14.6 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from scikit-learn==1.0.2) (1.22.3)\n",
      "Requirement already satisfied: scipy>=1.1.0 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from scikit-learn==1.0.2) (1.7.3)\n",
      "Collecting joblib>=0.11\n",
      "  Downloading joblib-1.1.0-py2.py3-none-any.whl (306 kB)\n",
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      "\u001b[?25hCollecting threadpoolctl>=2.0.0\n",
      "  Downloading threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
      "Successfully installed joblib-1.1.0 scikit-learn-1.0.2 threadpoolctl-3.1.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling pip package python-dateutil 2.8.2\n",
      "Requirement already satisfied: python-dateutil==2.8.2 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in ./blender-3.1.0-linux-x64/custom-python-packages/lib/python3.10/site-packages (from python-dateutil==2.8.2) (1.16.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mDevice Tesla K80 of type CUDA found and used.\n",
      "(  0.0001 sec |   0.0001 sec) Importing OBJ 'examples/resources/scene.obj'...\n",
      "  (  0.0002 sec |   0.0001 sec) Parsing OBJ file...\n",
      "    (  0.0747 sec |   0.0745 sec) Done, loading materials and images...\n",
      "    (  0.0856 sec |   0.0854 sec) Done, building geometries (verts:841 faces:854 materials: 9 smoothgroups:0) ...\n",
      "    (  0.0985 sec |   0.0983 sec) Done.\n",
      "  (  0.0990 sec |   0.0989 sec) Finished importing: 'examples/resources/scene.obj'\n",
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      "Fra:0 Mem:10.30M (Peak 10.61M) | Time:00:00.12 | Mem:0.00M, Peak:0.00M | Scene, ViewLayer | Initializing\n",
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      "Fra:0 Mem:10.30M (Peak 10.61M) | Time:00:00.12 | Mem:0.00M, Peak:0.00M | Scene, ViewLayer | Loading render kernels (may take a few minutes the first time)\n",
      "Fra:0 Mem:10.30M (Peak 18.31M) | Time:00:00.17 | Mem:0.00M, Peak:0.00M | Scene, ViewLayer | Updating Scene\n",
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      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:04.25 | Remaining:00:27.51 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 128/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:04.99 | Remaining:00:28.57 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 144/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:05.05 | Remaining:00:25.59 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 160/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:05.79 | Remaining:00:26.36 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 176/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:05.84 | Remaining:00:23.95 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 192/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:06.39 | Remaining:00:23.84 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 208/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:06.44 | Remaining:00:21.89 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 224/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:06.87 | Remaining:00:21.42 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 240/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:06.92 | Remaining:00:19.81 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 256/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:07.30 | Remaining:00:19.30 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 272/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:07.34 | Remaining:00:17.95 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 288/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:07.64 | Remaining:00:17.34 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 304/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:07.68 | Remaining:00:16.19 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 320/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:07.93 | Remaining:00:15.58 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 336/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:07.97 | Remaining:00:14.60 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 352/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.20 | Remaining:00:14.05 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 368/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.24 | Remaining:00:13.20 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 384/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.40 | Remaining:00:12.61 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 400/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.44 | Remaining:00:11.87 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 416/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.60 | Remaining:00:11.35 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 432/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.63 | Remaining:00:10.69 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 448/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.77 | Remaining:00:10.20 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 464/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.81 | Remaining:00:09.62 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 480/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.92 | Remaining:00:09.16 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 496/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:08.96 | Remaining:00:08.64 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 512/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.06 | Remaining:00:08.21 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 528/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.10 | Remaining:00:07.74 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 544/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.18 | Remaining:00:07.34 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 560/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.22 | Remaining:00:06.92 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 576/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.32 | Remaining:00:06.56 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 592/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.35 | Remaining:00:06.18 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 608/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.44 | Remaining:00:05.84 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 624/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.47 | Remaining:00:05.49 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 640/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.56 | Remaining:00:05.18 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 656/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.59 | Remaining:00:04.86 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 672/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.68 | Remaining:00:04.57 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 688/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.71 | Remaining:00:04.27 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 704/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.78 | Remaining:00:03.99 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 720/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.81 | Remaining:00:03.71 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 736/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.89 | Remaining:00:03.46 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 752/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.92 | Remaining:00:03.20 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 768/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:09.99 | Remaining:00:02.96 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 784/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.02 | Remaining:00:02.71 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 800/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.08 | Remaining:00:02.48 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 816/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.11 | Remaining:00:02.26 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 832/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.15 | Remaining:00:02.04 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 848/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.18 | Remaining:00:01.82 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 864/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.26 | Remaining:00:01.62 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 880/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.29 | Remaining:00:01.42 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 896/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.35 | Remaining:00:01.23 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 912/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.38 | Remaining:00:01.04 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 928/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.43 | Remaining:00:00.85 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 944/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.46 | Remaining:00:00.67 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 960/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.50 | Remaining:00:00.50 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 976/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.53 | Remaining:00:00.32 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 992/1024\n",
      "Fra:0 Mem:31.15M (Peak 55.93M) | Time:00:10.56 | Remaining:00:00.16 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 1008/1024\n",
      "Fra:0 Mem:42.15M (Peak 57.15M) | Time:00:10.63 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 1024/1024\n",
      "Fra:0 Mem:42.15M (Peak 57.15M) | Time:00:10.63 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Finished\n",
      "Fra:0 Mem:42.16M (Peak 57.15M) | Time:00:10.63 | Compositing\n",
      "Fra:0 Mem:42.16M (Peak 57.15M) | Time:00:10.63 | Compositing | Determining resolution\n",
      "Fra:0 Mem:42.16M (Peak 57.15M) | Time:00:10.63 | Compositing | Initializing execution\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.19 | Compositing | Tile 2-4\n",
      "| Time:00:15.19 | Compositing | Tile 1-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.19 | Compositing | Tile 3-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.19 | Compositing | Tile 4-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.22 | Compositing | Tile 1-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.22 | Compositing | Tile 2-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.25 | Compositing | Tile 3-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.26 | Compositing | Tile 4-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.26 | Compositing | Tile 1-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.26 | Compositing | Tile 2-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.26 | Compositing | Tile 3-4\n",
      "Fra:0 Mem:85.23M (Peak 85.23M) | Time:00:15.27 | Compositing | Tile 4-4\n",
      "Fra:0 Mem:85.16M (Peak 85.23M) | Time:00:15.27 | Compositing | De-initializing execution\n",
      "Saved: /dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff/normals_0000.exr\n",
      "Saved: /dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff/depth_0000.exr\n",
      "Saved: '/dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff/rgb_0000.png'\n",
      " Time: 00:15.69 (Saving: 00:00.34)\n",
      "\n",
      "Fra:1 Mem:31.16M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Initializing\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Waiting for render to start\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Scene\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Shaders\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Background\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Camera\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Objects\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Particle Systems\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Meshes\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Objects Flags\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Primitive Offsets\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Images\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Camera Volume\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Lookup Tables\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Lights\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Integrator\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Film\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Lookup Tables\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Baking\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Updating Device | Writing constant memory\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.00 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Sample 0/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.13 | Remaining:02:13.19 | Mem:0.51M, Peak:0.51M | Scene, ViewLayer | Sample 1/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.17 | Remaining:01:29.80 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 2/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.25 | Remaining:01:04.59 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 4/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.38 | Remaining:00:48.19 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 8/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.50 | Remaining:00:42.73 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 12/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.62 | Remaining:00:39.05 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 16/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:00.80 | Remaining:00:33.56 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 24/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:01.97 | Remaining:00:23.30 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 80/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:02.09 | Remaining:00:20.22 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 96/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:05.42 | Remaining:00:44.14 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 112/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:05.51 | Remaining:00:38.60 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 128/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:07.91 | Remaining:00:48.37 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 144/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:07.99 | Remaining:00:43.16 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 160/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:09.81 | Remaining:00:47.27 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 176/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:09.88 | Remaining:00:42.80 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 192/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:11.37 | Remaining:00:44.60 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 208/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:11.43 | Remaining:00:40.81 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 224/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:12.69 | Remaining:00:41.46 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 240/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:12.75 | Remaining:00:38.24 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 256/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:13.72 | Remaining:00:37.93 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 272/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:13.77 | Remaining:00:35.19 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 288/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:14.53 | Remaining:00:34.41 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 304/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:14.57 | Remaining:00:32.06 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 320/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:15.23 | Remaining:00:31.19 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 336/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:15.28 | Remaining:00:29.16 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 352/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:15.70 | Remaining:00:27.99 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 368/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:15.75 | Remaining:00:26.25 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 384/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:16.12 | Remaining:00:25.15 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 400/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:16.16 | Remaining:00:23.62 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 416/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:16.47 | Remaining:00:22.57 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 432/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:16.51 | Remaining:00:21.23 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 448/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:16.83 | Remaining:00:20.32 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 464/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:16.87 | Remaining:00:19.12 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 480/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.13 | Remaining:00:18.23 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 496/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.17 | Remaining:00:17.17 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 512/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.36 | Remaining:00:16.31 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 528/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.40 | Remaining:00:15.35 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 544/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.58 | Remaining:00:14.57 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 560/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.62 | Remaining:00:13.70 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 576/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.68 | Remaining:00:12.90 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 592/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.72 | Remaining:00:12.12 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 608/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.90 | Remaining:00:11.47 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 624/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:17.93 | Remaining:00:10.76 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 640/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.07 | Remaining:00:10.14 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 656/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.11 | Remaining:00:09.48 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 672/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.14 | Remaining:00:08.86 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 688/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.28 | Remaining:00:08.31 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 704/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.31 | Remaining:00:07.73 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 720/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.42 | Remaining:00:07.21 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 736/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.46 | Remaining:00:06.67 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 752/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.54 | Remaining:00:06.17 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 768/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.57 | Remaining:00:05.68 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 784/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.66 | Remaining:00:05.22 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 800/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.69 | Remaining:00:04.76 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 816/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.77 | Remaining:00:04.33 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 832/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.81 | Remaining:00:03.90 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 848/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.87 | Remaining:00:03.49 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 864/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.90 | Remaining:00:03.09 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 880/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:18.97 | Remaining:00:02.71 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 896/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.00 | Remaining:00:02.33 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 912/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.07 | Remaining:00:01.97 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 928/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.10 | Remaining:00:01.61 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 944/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.18 | Remaining:00:01.27 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 960/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.21 | Remaining:00:00.94 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 976/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.27 | Remaining:00:00.62 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 992/1024\n",
      "Fra:1 Mem:31.15M (Peak 85.23M) | Time:00:19.30 | Remaining:00:00.30 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 1008/1024\n",
      "Fra:1 Mem:42.15M (Peak 85.23M) | Time:00:19.38 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Sample 1024/1024\n",
      "Fra:1 Mem:42.15M (Peak 85.23M) | Time:00:19.38 | Mem:0.51M, Peak:16.51M | Scene, ViewLayer | Finished\n",
      "Fra:1 Mem:42.16M (Peak 85.23M) | Time:00:19.38 | Compositing\n",
      "Fra:1 Mem:42.16M (Peak 85.23M) | Time:00:19.38 | Compositing | Determining resolution\n",
      "Fra:1 Mem:42.16M (Peak 85.23M) | Time:00:19.39 | Compositing | Initializing execution\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.92 | Compositing | Tile 1-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.92 | Compositing | Tile 2-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.93 | Compositing | Tile 3-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.93 | Compositing | Tile 4-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.96 | Compositing | Tile 1-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.96 | Compositing | Tile 2-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.98 | Compositing | Tile 3-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:23.99 | Compositing | Tile 4-4\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:24.00 | Compositing | Tile 1-4Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:24.00 | Compositing | Tile 2-4\n",
      "\n",
      "Fra:1 Mem:85.23M (Peak 85.23M) Fra:1 Mem:85.23M (Peak 85.23M) | Time:00:24.00 | Compositing | Tile 3-4\n",
      "| Time:00:24.00 | Compositing | Tile 4-4\n",
      "Fra:1 Mem:85.16M (Peak 85.23M) | Time:00:24.00 | Compositing | De-initializing execution\n",
      "Saved: /dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff/normals_0001.exr\n",
      "Saved: /dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff/depth_0001.exr\n",
      "Saved: '/dev/shm/blender_proc_d9c99850fccd4592b665c791fb5704ff/rgb_0001.png'\n",
      " Time: 00:24.13 (Saving: 00:00.04)\n",
      "\n",
      "It seems the freeimage library which is necessary to read .exr files cannot be found on your computer.\n",
      "Gonna try to download it automatically.\n",
      "Imageio: 'libfreeimage-3.16.0-linux64.so' was not found on your computer; downloading it now.\n",
      "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/freeimage/libfreeimage-3.16.0-linux64.so (4.6 MB)\n",
      "Downloading: 8192/4830080 bytes (0.2%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b4830080/4830080 bytes (100.0%)\n",
      "  Done\n",
      "File saved as /root/.imageio/freeimage/libfreeimage-3.16.0-linux64.so.\n",
      "Merging data for frame 0 into examples/basics/basic/output/0.hdf5\n",
      "Merging data for frame 1 into examples/basics/basic/output/1.hdf5\n",
      "\n",
      "Blender quit\n",
      "Cleaning temporary directory\n"
     ]
    }
   ],
   "source": [
    "# run the BlenderProc basic example\n",
    "!blenderproc run examples/basics/basic/main.py examples/resources/camera_positions examples/resources/scene.obj examples/basics/basic/output --blender-install-path ./"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xbyP7EC4T9kc",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We visualize the first rendered color, depth and normal image which corresponds to the `0.hdf5` file inside the `examples/basics/basic/output` folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 846
    },
    "id": "6PeHFCV2HbbA",
    "outputId": "32e0e797-2bb9-4e64-85f3-9b5f9f4f6a19",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "examples/basics/basic/output/0.hdf5: \n",
      "Keys: 'colors': (512, 512, 3), 'depth': (512, 512), 'normals': (512, 512, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the generated data (0.hdf5)\n",
    "%run \"-m\" \"blenderproc\" \"vis\" \"hdf5\" \"examples/basics/basic/output/0.hdf5\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fg72XDwWTbhf",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We visualize the second rendered color, depth and normal image which corresponds to the `1.hdf5` file inside the `examples/basics/basic/output` folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 846
    },
    "id": "u0YuChqdzX2p",
    "outputId": "c01a5a7e-5fe2-4b39-83c6-86e31d9aa9a2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "examples/basics/basic/output/1.hdf5: \n",
      "Keys: 'colors': (512, 512, 3), 'depth': (512, 512), 'normals': (512, 512, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the generated data (1.hdf5)\n",
    "%run \"-m\" \"blenderproc\" \"vis\" \"hdf5\" \"examples/basics/basic/output/1.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XXTHqas_WyYb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "basic_example.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
